Name
Abstract | ||
|---|---|---|
Research into knowledge acquisition for robotic agents has looked at interpreting natural language instructions meant for humans into robot-executable programs; however, the ambiguities of natural language remain a challenge for such "translations". In this paper, we look at a particular sort of ambiguity: the control flow structure of the program described by the natural language instruction. It is not always clear, when more conditional statements appear in a natural language instruction, which of the conditions are to be thought of as alternative options in the same test, and which belong to a code branch triggered by a previous conditional. We augment a system which uses probabilistic reasoning to identify the meaning of the words in a sentence with reasoning about action preconditions and effects in order to filter out non-sensical code structures. We test our system with sample instruction sheets inspired from analytical chemistry. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-67190-1_30 | Lecture Notes in Artificial Intelligence |
DocType | Volume | ISSN |
Conference | 10505 | 0302-9743 |
Citations | PageRank | References |
1 | 0.39 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mihai Pomarlan | 1 | 1 | 0.72 |
| Sebastian Koralewski | 2 | 2 | 2.43 |
| Michael Beetz | 3 | 3784 | 284.03 |